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I have a large dataframe made up of 8 different datasets, sample of three with different time zones below. I import the data and then rbind the 8 dataframes into one df. I want to be able to run analysis and ggplot the whole dataset or sections of it keeping the Date_time timezone correct to each area.

I recently found that dataframes in R apparently don't support multiple timezones in one column, so all following df added using rbind take on the timezone of the first data (df$Date_time changes from imported value), I've tried different ways to overcome this issue:

1. Saving the dataframes as a list, and creating a list of the 8 lists keeps the local time zone correct in listx$Date_time but ggplot doesn't allow plotting from a list, and when i convert back to dataframe the timezones are all changed again.

2. I created df$tz column to have each time zone in the file and I tried this: solution to create a fixed UTC time but this is a character string df$UTC and I'm not sure how to make R and ggplot plot the data with the correct timezone information.

df$Date remains true to the different timezones because time is stripped, and df$time_ser remains correct as the date is stripped.

Do I need to include a function to calculate the actual local time in every analysis I do as it runs - very time consuming, or is there some other way to do this.

Sample Dataframe 'df' with three time zones:

I am trying to create an identity matrix from a dataframe. The dataframe is like so:

Very similar questions have been asked here, here, and here. However, they all seem to rely on knowing the column names of the data.

I am trying to get the column index of a data frame that matches a numeric vector. For example, if I have some data and a vector like so:

I am trying to split a column of comma separated characters into several columns (as many as different characters overall). I read similar questions such as this:

Split data frame string column into multiple columns

But the solutions were based on a small number of possible characters, so that the new columns could be named in advanced before the columns was split.

This is my example:

there has been a similar question to mine 6 years+ ago and it hasn't been solve (R -- Can I apply the train function in caret to a list of data frames?) This is why I am bringing up this topic again.

I'm writing my own functions for my big R project at the moment and I'm wondering if there is an opportunity to sum up the model training function train() of the pakage caret for different dataframes with different predictors. My function should look like this:

I have a matrix with many rows and columns, of the nature

My current data-frame is:

I would like to generate a hexagonal lattice heat-map in which each cell represents a group. Likewise, each cell would be a hexagon with a unique color (fill, set by a column color in the data-frame) value, and a saturation (alpha) value corresponding to continuous decimal values from a chemical concentration dateset.

I would like to use a standardized data format which would allow me to quickly construct figures based on standardized datasets containing 25 groups.

For example, a datasheet would look like this: